We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events by synthesizing structural estimation with algorithmic machine learning to quantify bias. We find that investors systematically overreact to perceptions about multiple fundamental shocks, a phenomenon we show often dampens rather than amplifies market volatility via a “shock composition effect.” Such effects imply that the stock market can underreact to news, even when investors overreact to all shocks.

This paper provides evidence that the U.S. dollar affects trade through a financial channel of the exchange rate. Using global data over three decades, we show that dollar appreciation increases import prices and decreases import quantities for non-U.S. dollar countries. In line with a financial channel, these effects are stronger when the exporting country borrows more in U.S. dollars abroad. The financial channel was active before the global financial crisis, has strengthened since, and operates independently of the dominant currency invoicing channel. Instrumenting the dollar is key to uncovering the full effect of the financial channel.

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A single macroeconomic factor based on growth in the capital share of aggregate income exhibits significant explanatory power for expected returns across a range of equity characteristic portfolios and nonequity asset classes, with risk price estimates that are of the same sign and similar in magnitude. Positive exposure to capital share risk earns a positive risk premium, commensurate with recent asset pricing models in which redistributive shocks shift the share of income between the wealthy, who finance consumption primarily out of asset ownership, and workers, who finance consumption primarily out of wages and salaries.

This paper provides a comprehensive survey of existing measures of uncertainty, risk, and volatility, noting their conceptual distinctions. It summarizes how they are constructed, their relative advantages in usage, and their effects on financial market and economic outcomes. The measures are divided into four categories based on the construction methodology: news-based, survey-based, econometric-based, and market-based measures. While heightened uncertainty is typically associated with negative real and financial outcomes, the magnitude of these effects and the interpretation of transmission channels crucially depend on identification considerations.